Subramaniam Sophini, Faisal Abu Ilius, Deen M Jamal
School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada.
Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada.
Front Digit Health. 2022 Jul 14;4:921506. doi: 10.3389/fdgth.2022.921506. eCollection 2022.
Fall risk assessment and fall detection are crucial for the prevention of adverse and long-term health outcomes. Wearable sensor systems have been used to assess fall risk and detect falls while providing additional meaningful information regarding gait characteristics. Commonly used wearable systems for this purpose are inertial measurement units (IMUs), which acquire data from accelerometers and gyroscopes. IMUs can be placed at various locations on the body to acquire motion data that can be further analyzed and interpreted. Insole-based devices are wearable systems that were also developed for fall risk assessment and fall detection. Insole-based systems are placed beneath the sole of the foot and typically obtain plantar pressure distribution data. Fall-related parameters have been investigated using inertial sensor-based and insole-based devices include, but are not limited to, center of pressure trajectory, postural stability, plantar pressure distribution and gait characteristics such as cadence, step length, single/double support ratio and stance/swing phase duration. The acquired data from inertial and insole-based systems can undergo various analysis techniques to provide meaningful information regarding an individual's fall risk or fall status. By assessing the merits and limitations of existing systems, future wearable sensors can be improved to allow for more accurate and convenient fall risk assessment. This article reviews inertial sensor-based and insole-based wearable devices that were developed for applications related to falls. This review identifies key points including spatiotemporal parameters, biomechanical gait parameters, physical activities and data analysis methods pertaining to recently developed systems, current challenges, and future perspectives.
跌倒风险评估和跌倒检测对于预防不良和长期健康后果至关重要。可穿戴传感器系统已被用于评估跌倒风险和检测跌倒,同时提供有关步态特征的其他有意义信息。为此目的常用的可穿戴系统是惯性测量单元(IMU),它从加速度计和陀螺仪获取数据。IMU可以放置在身体的各个位置以获取可进一步分析和解释的运动数据。基于鞋垫的设备是也为跌倒风险评估和跌倒检测而开发的可穿戴系统。基于鞋垫的系统放置在脚底下方,通常获取足底压力分布数据。使用基于惯性传感器和基于鞋垫的设备研究的与跌倒相关的参数包括但不限于压力中心轨迹、姿势稳定性、足底压力分布以及步态特征,如步频、步长、单/双支撑比和站立/摆动相持续时间。从基于惯性和鞋垫的系统获取的数据可以采用各种分析技术,以提供有关个人跌倒风险或跌倒状态的有意义信息。通过评估现有系统的优点和局限性,可以改进未来的可穿戴传感器,以实现更准确和方便的跌倒风险评估。本文综述了为与跌倒相关的应用而开发的基于惯性传感器和基于鞋垫的可穿戴设备。本综述确定了关键要点,包括与最近开发的系统相关的时空参数、生物力学步态参数、身体活动和数据分析方法、当前挑战以及未来展望。